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Record W2899846820 · doi:10.1115/ipc2018-78311

Standardization of Flattening Procedure of Transverse to Pipe Axis Strap Tensile Samples

2018· article· en· W2899846820 on OpenAlexaff
Muhammad Rashid, S. Chen, Laurie Collins

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsFlatteningMaterials scienceUltimate tensile strengthTransverse planeFixtureStructural engineeringComposite materialGrindingEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Tensile testing on large diameter line pipe is generally done using strap samples obtained in the transverse to pipe axis (TPA) orientation of a pipe. The strap samples are then flattened and machined prior to testing. Although the standardized tensile testing is well documented, the variability in the reported TPA tensile properties of the same material tested within a lab or at different labs has always been an issue. Recent work conducted at EVRAZ NA research lab has identified flattening as the main source of the variability in reported yield strength (YS) values for line pipe. The lack of a standard procedure for flattening TPA strap samples is a major obstacle to obtaining consistent results. Therefore, the main objective of this current study was to establish a standardized flattening procedure for TPA strap samples. Both finite element analysis (FEA) and experimental approaches were adopted. Various flattening methods and fixtures were studied. Extensive flattening experiments were conducted on TPA samples from different line pipe products. Results showed that the spring back after flattening in a TPA sample is different for pipes with different gauge and grades. It was established that consistent flattening can be achieved using appropriate fixtures for differerent ranges of tubular products defined by grade, diameter and gauges. Evaluation of the flattening fixture designs and experimental results are discussed in this paper.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.219
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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